A chronic wound, as defined by Centers for Medicare and Medicaid Services, is a wound that has not healed in 30 days. An estimated 6.5 million patients in the United States are affected by chronic wounds, and it is claimed that an excess of US$25 billion is spent annually on treatment of chronic wounds. The burden is growing rapidly due to increasing health care costs, an aging population and a sharp rise in the incidence of diabetes and obesity worldwide. The current state of the art approach in measuring wound size using digital images, known as digital planimetry, requires the clinician to identify wound borders and wound tissue type within the image. This is a time-intensive process and is a barrier to achieving clinical quality benchmarks.
Although wound segmentation from photographic images has been the subject of several studies, most of the work in this area deals with images that are either acquired under controlled imaging conditions, confined to wound region only, or narrowed to specific types of wounds. Because these restrictions are mostly impractical for clinical conditions, there is a need to develop image segmentation methods that will work with images acquired in regular clinical conditions.
Current works in wound segmentation and monitoring as well as existing software tools are as follows. Wannous et al. compared the mean shift, JSEG and CSC techniques in segmenting 25 wound images, before extracting color and textural features to classify the tissues into granulation, slough and necrosis using an SVM classifier. The wound images were taken with respect to a specific protocol integrating several points of views for each single wound, which includes using a ring flash with specific control and placing a calibrated Macbeth color checker pattern near the wounds. They reported that both segmentation and classification work better on granulation than slough and necrosis. Hettiarachchi et al. attempted wound segmentation and measurement in a mobile setting. The segmentation is based on active contour models which identifies the wound border irrespective of coloration and shape. The active contour process was modified by changing the energy calculation to minimize points sticking together as well as including pre-processing techniques to reduce errors from artifacts and lighting conditions. Although the accuracy was reported to be 90%, the method is rather sensitive to camera distance, angle and lighting conditions.
In a work by Veredas et al., a hybrid approach based on neural networks and Bayesian classifiers is proposed in the design of a computational system for tissue identification and labeling in wound images. Mean shift and region-growing strategy are implemented for region segmentation. The neural network and Bayesian classifiers are then used to categorize the tissue based on color and texture features extracted from the segmented regions, with 78.7% sensitivity, 94.7% specificity and 91.5% accuracy reported. Hani et al. presented an approach based on utilizing hemoglobin content in chronic ulcers as an image marker to detect the growth of granulation tissue. Independent Component Analysis is employed to extract grey level hemoglobin images from Red-Green-Blue (RGB) color images of chronic ulcers. Data clustering techniques are then implemented to classify and segment detected regions of granulation tissue from the extracted hemoglobin images. 88.2% sensitivity and 98.8% specificity were reported on a database of 30 images.
Perez et al. proposed a method for the segmentation and analysis of leg ulcer tissues in color images. The segmentation is obtained through analysis of the red, green, blue, saturation and intensity channels of the image. The algorithm, however, requires the user to provide samples of the wound and the background before the segmentation can be carried out. Wantanajittikul et al. employs the Cr-transformation, Luv-transformation and fuzzy c-means clustering technique to separate the burn wound area from healthy skin before applying mathematical morphology to reduce segmentation errors. To identify the degree of the burns, h-transformation and texture analysis are used to extract feature vectors for SVM classification. Positive predictive value and sensitivity between 72.0% and 98.0% were reported in segmenting burn areas in five images, with 75.0% classification accuracy.
Song and Sacan proposed a system capable of automatic image segmentation and wound region identification. Several commonly used segmentation methods (k-means clustering, edge detection, thresholding, and region growing) are utilized to obtain a collection of candidate wound regions. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) are then applied with supervised learning in the prediction procedure for the wound identification. Experiments on 92 images from 14 patients (78 training, 14 testing) showed that both MLP and RBF have decent efficiency, with their own advantages and disadvantages. Kolesnik and Fexa used color and textural features from 3-D color histogram, local binary pattern and local contrast variation with the support vector machine (SVM) classifier to segment 23 wound images based on 50 manually segmented training images. The SVM generated wound boundary is further refined using deformable snake adjustment. Although this study does not have the aforementioned restrictions (i.e. acquired under controlled imaging conditions, confined to wound region only, or narrowed to specific types of wounds), results were reported on a relatively small set of images. An average error rate of 6.6%, 22.2% and 5.8% were reported for the color, texture and hybrid features, respectively.
In addition to wound segmentation, wound healing and monitoring have been the subject of several studies on wound image analysis. Cukjati et al. presented their findings on how the wound-healing rate should be defined to enable appropriate description of wound healing dynamics. They suggested that wound area measurements should be transformed to percentage of initial wound area and fitted to a delayed exponential model. In the suggested model, the wound healing rate is described by the slope of the curve is fitted to the normalized wound area measurements over time after initialization delay. Loizou et al. established a standardized and objective technique to assess the progress of wound healing in a foot. They concluded that while none of the geometrical features (area, perimeter, x-, y-coordinate) show significant changes between visits, several texture features (mean, contrast, entropy, SSV, sum variance, sum average) do, indicating these features might provide a better wound healing rate indication. Finally, Burns et al. evaluated several methods for quantitative wound assessment on diabetic foot ulcers, namely wound volume, wound area, and wound coloration.
There are also quite a few software tools for wound analysis and monitoring currently available. All the software, however, has yet to incorporate automated or semi-automated wound detection or segmentation so that the clinician's initial involvement can be minimized. For example, PictZar™ Digital Planimetry Software (PictZar.com, Elmwood Park, N.J.) is commercial software for wound analysis which provides measurements such as length, width, surface area, circumference, and estimated volume to the users. The software, however, does not incorporate automated or semi-automated wound detection; instead it requires user drawings and calibration for the above measurements to be computed. Filko et al. developed WITA, a color image processing software application that has the capability to analyze digital wound images, and based on learned tissue samples, the program classifies the tissue and monitors wound healing. The wound tissue types are divided into black necrotic eschar, yellow fibrin or slough, red granulation tissue and unclassified parts of the image, although no evaluation against the known ground truth was presented for the image analysis part of the software. To obtain wound dimensions, users must mark the distance on the photograph that is equivalent to 1 cm (or 1 inch). A different approach to wound monitoring software and hardware was proposed by Weber et al. They developed a new “wound mapping” device, which is based on electrical impedance spectroscopy and involves the multi-frequency characterization of the electrical properties of wound tissue under an electrode array. This approach, however, requires major changes to the daily clinical routine in wound care.
Therefore, systems and methods are desired that overcome challenges in the art, some of which are described above. There is a need for a timely and accurate method to document the size and evolving nature of chronic wounds in both the inpatient and outpatient settings. Such an application can potentially reduce clinicians' workload considerably; make the treatment and care more consistent and accurate; increase the quality of documentation in the medical record and enable clinicians to achieve quality benchmarks for wound care as determined by the Center for Medicare Services.